Computational geometry is concerned with the design, analysis, and implementation of algorithms for geometric and topological problems, which arise naturally in a wide range of areas, including computer graphics, robotics, geographic information systems, molecular biology, sensor networks, machine learning, data mining, scientific computing, theoretical computer science, and pure mathematics. Computational geometry is a vibrant and mature field of research, with several dedicated international conferences and journals, significant real-world impact, and strong intellectual connections with other computing and mathematics disciplines.

The emphasis of the seminar will be on presenting recent developments in computational geometry, as well as identifying new challenges, opportunities, and connections to other fields of computing. In addition to the usual broad coverage of emerging results in the field, the seminar will include invited survey talks on two broad and overlapping focus areas that cover a wide range of both theoretical and practical issues in geometric computing. Both focus areas have seen exciting recent progress and offer numerous opportunities for further cross-disciplinary impact.

Computational topology. Over the last decade, computational topology has grown from an important subfield of computational geometry into a mature research area in its own right. Results in this field combine classical mathematical techniques from combinatorial, geometric, and algebraic topology with algorithmic tools from computational geometry and optimization. Key developments in this area include algorithms for modeling and reconstructing surfaces from point-cloud data, algorithms for shape matching and classification, topological graph algorithms, new generalizations of persistent homology, practical techniques for experimental low-dimensional topology, and new fundamental results on the computability and complexity of embedding problems. These results have found a wide range of practical applications in computer graphics, computer vision, robotics, sensor networks, molecular biology, data analysis, and experimental mathematics.

Geometric data analysis. Geometric data sets are being generated at an unprecedented scale from many different sources, including digital video cameras, satellites, sensor networks, and physical simulations. The need to manage, analyze, and visualize dynamic, large-scale, high-dimensional, noisy data has raised significant theoretical and practical challenges not addressed by classical geometric algorithms. Key developments in this area include new computational models for massive, dynamic, and distributed geometric data; new techniques for effective dimensionality reduction; approximation algorithms based on coresets and other sampling techniques; algorithms for noisy and uncertain geometric data; and geometric algorithms for information spaces. Results in this area draw on mathematical tools from statistics, linear algebra, functional analysis, metric geometry, geometric and differential topology, and optimization, and they have found practical applications in spatial databases, clustering, shape matching and analysis, machine learning, computer vision, and scientific visualization.

• Peyman Afshani (Aarhus University, DK) [dblp]
• Annamaria Amenta (University of California - Davis, US) [dblp]
• Franz Aurenhammer (TU Graz, AT) [dblp]
• Maike Buchin (Ruhr-Universität Bochum, DE) [dblp]
• Sergio Cabello (University of Ljubljana, SI) [dblp]
• Siu-Wing Cheng (HKUST - Kowloon, HK) [dblp]
• Otfried Cheong (KAIST - Daejeon, KR) [dblp]
• Jinhee Chun (Tohoku University - Sendai, JP) [dblp]
• Mark de Berg (TU Eindhoven, NL) [dblp]
• Vin de Silva (Pomona College - Claremont, US) [dblp]
• Olivier Devillers (INRIA Lorraine - Nancy, FR) [dblp]
• Tamal K. Dey (Ohio State University - Columbus, US) [dblp]
• Michael Gene Dobbins (Postech - Pohang, KR) [dblp]
• Anne Driemel (TU Eindhoven, NL) [dblp]
• Ioannis Emiris (University of Athens, GR) [dblp]
• Jeff Erickson (University of Illinois - Urbana-Champaign, US) [dblp]
• Jie Gao (SUNY - Stony Brook, US) [dblp]
• Marc Glisse (INRIA Saclay - Île-de-France, FR) [dblp]
• Leonidas J. Guibas (Stanford University, US) [dblp]
• Michael Kerber (MPI für Informatik - Saarbrücken, DE) [dblp]
• David G. Kirkpatrick (University of British Columbia - Vancouver, CA) [dblp]
• Rolf Klein (Universität Bonn, DE) [dblp]
• Joseph S. B. Mitchell (SUNY - Stony Brook, US) [dblp]
• Wolfgang Mulzer (FU Berlin, DE) [dblp]
• Elizabeth Munch (University of Albany, US) [dblp]
• Yota Otachi (JAIST - Ishikawa, JP) [dblp]
• Jeff M. Phillips (University of Utah - Salt Lake City, US) [dblp]
• Natan Rubin (Ben Gurion University - Beer Sheva, IL) [dblp]
• Vera Sacristán Adinolfi (UPC - Barcelona, ES) [dblp]
• Maria Saumell (University of West Bohemia - Pilsen, CZ) [dblp]
• Ludmila Scharf (FU Berlin, DE) [dblp]
• André Schulz (Universität Münster, DE) [dblp]
• Raimund Seidel (Universität des Saarlandes, DE) [dblp]
• Donald Sheehy (University of Connecticut - Storrs, US) [dblp]
• Jonathan Shewchuk (University of California - Berkeley, US) [dblp]
• Anastasios Sidiropoulos (Ohio State University - Columbus, US) [dblp]
• Fabian Stehn (Universität Bayreuth, DE) [dblp]
• Monique Teillaud (INRIA Lorraine - Nancy, FR) [dblp]
• Csaba Tóth (California State University - Northridge, US) [dblp]
• Carola Wenk (Tulane University, US) [dblp]
• Nicola Wolpert (University of Applied Sciences - Stuttgart, DE) [dblp]